You're reading from Data Engineering with Python Work with massive datasets to design data models and automate data pipelines using Python

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781839214189

Length 356 pages

Edition 1st Edition

Languages

Python

Concepts

Data Analysis

Author (1):

Paul Crickard

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Table of Contents (21) Chapters

Preface

1. Section 1: Building Data Pipelines – Extract Transform, and Load

2. Chapter 1: What is Data Engineering? FREE CHAPTER

3. Chapter 2: Building Our Data Engineering Infrastructure

4. Chapter 3: Reading and Writing Files

5. Chapter 4: Working with Databases

6. Chapter 5: Cleaning, Transforming, and Enriching Data

7. Chapter 6: Building a 311 Data Pipeline

8. Section 2:Deploying Data Pipelines in Production

9. Chapter 7: Features of a Production Pipeline

10. Chapter 8: Version Control with the NiFi Registry

11. Chapter 9: Monitoring Data Pipelines

12. Chapter 10: Deploying Data Pipelines

13. Chapter 11: Building a Production Data Pipeline

14. Section 3:Beyond Batch – Building Real-Time Data Pipelines

15. Chapter 12: Building a Kafka Cluster

16. Chapter 13: Streaming Data with Apache Kafka

17. Chapter 14: Data Processing with Apache Spark

18. Chapter 15: Real-Time Edge Data with MiNiFi, Kafka, and Spark

19. Other Books You May Enjoy

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Appendix

Chapter 2: Building Our Data Engineering Infrastructure

In the previous chapter, you learned what data engineers do and their roles and responsibilities. You were also introduced to some of the tools that they use, primarily the different types of databases, programming languages, and data pipeline creation and scheduling tools.

In this chapter, you will install and configure several tools that will help you throughout the rest of this book. You will learn how to install and configure two different databases – PostgreSQL and Elasticsearch – two tools to assist in building workflows – Airflow and Apache NiFi, and two administrative tools – pgAdmin for PostgreSQL and Kibana for Elasticsearch.

With these tools, you will be able to write data engineering pipelines to move data from one source to another and also be able to visualize the results. As you learn how to build pipelines, being able to see the data and how it has transformed will be useful to...